ABSTRACT Sharp falls or explosive growths in exchange markets, whether expected or not, generates new challenges for investors who want to protect their investments or achieve an optimum benefit during and after the turmoil. An anomaly of the exchange market, instigated by the Swiss National Bank, occurred when the Swiss Franc decoupled from the euro unexpectedly. The United Kingdom (UK) vote to withdraw from the European Union (Brexit), in contrast, was feared but expected. A comparison of the consequences of the anomalies gives us an unprecedented opportunity to investigate prediction capabilities of the EVOLINO Recurrent Neural Network Ensemble (ERNN) model following an anomaly. By introducing this new information to the ERNN model and analyzing its response, we increase investor resources during large exchange rate fluctuations; this will provide them with additional information that will help them construct different portfolios. Reaction to the anomaly was visible only after the anomaly occurred, this is when the model began to acquire data influenced by the extreme change. Comparing different strategies which are related or unrelated to the anomaly and orthogonal or not orthogonal for conservative, moderate, or aggressive trading shows that in order to profit from the anomaly, speculation depends on prediction-accuracy and on the sets of exchange-rate associated with the anomaly.

中文翻译：

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Evolino 循环神经网络集成用于异常时期的交易市场投机

摘要 外汇市场的急剧下跌或爆炸性增长，无论是否预期，都会给希望在动荡期间和之后保护其投资或获得最佳收益的投资者带来新的挑战。当瑞士法郎与欧元意外脱钩时，在瑞士国家银行的煽动下，外汇市场出现了异常现象。相比之下，英国 (UK) 投票退出欧盟 (Brexit) 令人担忧但在意料之中。异常后果的比较为我们提供了一个前所未有的机会来研究 EVOLINO 循环神经网络集成 (ERNN) 模型在异常之后的预测能力。通过将这些新信息引入 ERNN 模型并分析其响应，我们可以在汇率大幅波动时增加投资者资源；这将为他们提供额外的信息，帮助他们构建不同的投资组合。对异常的反应只有在异常发生后才可见，此时模型开始获取受极端变化影响的数据。比较与异常相关或不相关的不同策略以及保守、适度或激进交易的正交或非正交策略表明，为了从异常中获利，投机取决于预测准确性和相关的汇率集异常。